为什么精确图像没有准确的描述符?

时间:2015-09-11 17:56:51

标签: c# opencv image-processing emgucv surf

我正在开发一个图像识别应用程序,有些东西让我烦恼。

我正在使用双色调图像(文档)。如果我制作相同图像文件的副本并比较两者(实际比较两个[相同子区域]的子区域),我不会使用SURF获得100%匹配。这是我的代码的一部分:

public double FindPercentMatch(Bitmap obj, Bitmap scene)
{

    // get scene key points
    VectorOfKeyPoint scenePoints = GetKeyPoints((Bitmap)scene.Clone());

    // if no scene points found, no need to go any further
    if (scenePoints == null || scenePoints.Size == 0) { return 0; }

    // get object key points
    VectorOfKeyPoint objectPoints = GetKeyPoints((Bitmap)obj.Clone());            

    // if not enough object key points found vs scene key points, then match can't be close enough (since
    // you can't have more matches than scene points anyway, so don't even try to match
    if (objectPoints == null || objectPoints.Size == 0 || (scenePoints.Size / objectPoints.Size < .15)) { return 0; }

    // we have enough key points, so compute descriptors for scene and object
    Matrix<float> objectDescriptors = GetDescriptors(objectPoints,(Bitmap)obj.Clone());
    Matrix<float> sceneDescriptors = GetDescriptors(scenePoints, (Bitmap)scene.Clone());
    int objectDescriptorCount = objectDescriptors == null ? 0 : objectDescriptors.Size.Height;
    int sceneDescriptorCount = sceneDescriptors == null ? 0 : sceneDescriptors.Size.Height;

    // find matches
    int sim = FindMatches(objectDescriptors, sceneDescriptors);

    // for testing so we know how many were found so we can monitor it
    log.Debug("descriptors1 = " + objectDescriptorCount + ", 2 = " + sceneDescriptorCount + ", matches=" + sim);

    double percent = 0;
    if (sim != 0)
    {
        percent = objectDescriptorCount != 0 ? (double)sim / (double)objectDescriptorCount : 0;
        log.Debug(percent * 100 + "%");
    }
    return percent;

}

public int FindMatches(Matrix<float> dbDescriptors, Matrix<float> queryDescriptors)
{

    double uniquenessThreshold = 0.6;

    if (dbDescriptors == null || queryDescriptors == null)
    {
        return 0;
    }
    var indices = new Matrix<int>(queryDescriptors.Rows, 2); // matrix that will contain indices of the 2-nearest neighbors found
    var dists = new Matrix<float>(queryDescriptors.Rows, 2); // matrix that will contain distances to the 2-nearest neighbors found

    // create FLANN index with 4 kd-trees and perform KNN search over it look for 2 nearest neighbours
    var flannIndex = new Index(dbDescriptors, 4);

    flannIndex.KnnSearch(queryDescriptors, indices, dists, 2, 24);

    // for eatch match over a certain threshold, add +1 to the number of 'good' matches
    int sim = 0;
    for (int i = 0; i < indices.Rows; i++)
    {
        // filter out all inadequate pairs based on distance between pairs
        if (dists.Data[i, 0] < (uniquenessThreshold * dists.Data[i, 1]))
        {
            sim++;
        }
    }
    return sim;
}

在图像的同一子区域使用FindPercentMatch时的预期结果将是100%,但根据图像,它可能是70%,99%,我甚至看到了101%。

2 个答案:

答案 0 :(得分:0)

据我所知,您的过滤不会导致1对1的objectdescriptor - scenedescriptor(匹配超过某个阈值),这就是为什么你可以得到100 +%

另一件事是&#34; objectDescriptorCount等于sceneDescriptorCount&#34;并不总是意味着&#34; objectDescriptors等于sceneDescriptors&#34;。手动检查描述符,如果它们相同则意味着您的处理逻辑出错了(计算&#34; sim&#34;)

希望有所帮助

答案 1 :(得分:0)

我认为我已经通过使用强力匹配器而不是flann索引修复了我的问题。此外,我没有使用2nn并进行比较,而是找到了第一个最近邻居,并将任何低于某个阈值的人计算为“良好匹配”。然后我将好匹配除以OBJECT(不是场景)的关键点总数,所以我现在最终得到0-100%的匹配。

        int numberOfNearestNeighbors = 1;

        double maxDistance = .30;

        BruteForceMatcher<float> matcher = new BruteForceMatcher<float>(DistanceType.L2);

        matcher.Add(dbDescriptors);

        var indices = new Matrix<int>(queryDescriptors.Rows, numberOfNearestNeighbors);
        using (Matrix<float> dist = new Matrix<float>(queryDescriptors.Rows, numberOfNearestNeighbors))
        {
            // null = no mask, use whole image
            matcher.KnnMatch(queryDescriptors, indices, dist, numberOfNearestNeighbors, null);

            int matchesUnderMaxDistance = 0;
            float distance = 0;

            // filter matches that are too different
            for (int i = 0; i < indices.Rows; i++)
            {
                distance = dist.Data[i, 0];

                if (distance < maxDistance)
                {
                    matchesUnderMaxDistance++;
                }
            }

            return matchesUnderMaxDistance;
        }